The last dataset on which we evaluated the \(L_{IL}\)  driven classification models is the Zhang CXR dataset [1]}. Unlike other datasets, the Zhang CXR dataset [1]} can only be utilized for binary classification tasks, i.e., to classify healthy and pneumonic pathologies. Therefore, to evaluate the capacity of the proposed loss function on the Zhang CXR dataset [1]}, we first trained all the classification models for recognizing only the healthy pathologies (i.e., we tuned all the models for a one-category classification task). Afterward, we incrementally molded them to recognize pneumonic pathologies via few training examples, where all the models are penalized so that they accurately learn distinct feature representation for the pneumonic pathologies (within CXR scans) while retaining their previous knowledge about the healthy pathologies. The classification performance of all the four classification networks on the Zhang CXR dataset [1]} is presented in Table REF . Here, we can see that the \(L_{IL}\)  driven ResNet-101 [5]} only lags from its fine-tuned variant by 6.50% in terms of accuracy and 5.45% in terms of F1 score. It should also be noted here while the incremental ResNet-101 [5]} uses a significantly lower amount of training samples compared to its fine-tuning variant, i.e., it just uses 522 (134 healthy and 388 pneumonia affected) scans out of 5,232 to identify these pathologies.
